suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(gapminder))

Part 1: Factor management

Elaboration for the gapminder data set

1.Drop Oceania

Let’s grab a sense of the gapminder at first:

# A summary of the gapmainder
str(gapminder)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...
# see what's going on inside by just showing the head rows:
knitr::kable(head(gapminder,15))
country continent year lifeExp pop gdpPercap
Afghanistan Asia 1952 28.801 8425333 779.4453
Afghanistan Asia 1957 30.332 9240934 820.8530
Afghanistan Asia 1962 31.997 10267083 853.1007
Afghanistan Asia 1967 34.020 11537966 836.1971
Afghanistan Asia 1972 36.088 13079460 739.9811
Afghanistan Asia 1977 38.438 14880372 786.1134
Afghanistan Asia 1982 39.854 12881816 978.0114
Afghanistan Asia 1987 40.822 13867957 852.3959
Afghanistan Asia 1992 41.674 16317921 649.3414
Afghanistan Asia 1997 41.763 22227415 635.3414
Afghanistan Asia 2002 42.129 25268405 726.7341
Afghanistan Asia 2007 43.828 31889923 974.5803
Albania Europe 1952 55.230 1282697 1601.0561
Albania Europe 1957 59.280 1476505 1942.2842
Albania Europe 1962 64.820 1728137 2312.8890
And check the continent le vels in gapminder : 
levels(gapminder$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

Now let’s drop ‘Oceania’, and see what will happen:

gap_drop <- gapminder %>% 
  filter(continent != 'Oceania')
str(gap_drop)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1680 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...
levels(gap_drop$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

It seems 24 rows have been removed (from 1704 to 1680). But the number of continent levels is still 5. And the “Oceania” level itself still exists. To verify that “Oceania” related rows were indeed dropped, let’s count the “Ociania” rows in gapminder and gap_drop:

gapminder %>% 
  filter(continent == "Oceania") %>% 
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1    24
gap_drop %>% 
  filter(continent == "Oceania") %>% 
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1     0

To get rid of the unused “Oceania” level:

drop1 <- gap_drop %>% 
  # operates on all the factors
  droplevels()
str(drop1)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1680 obs. of  6 variables:
##  $ country  : Factor w/ 140 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 4 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...
levels(drop1$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"
drop2 <- gap_drop$continent %>% 
  # operates on a factor
  fct_drop()
str(drop2)
##  Factor w/ 4 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
levels(drop2)
## [1] "Africa"   "Americas" "Asia"     "Europe"

Now we do have 4 continent levels.

2.Reorder the levels of country or continent.

Let’s try different orders. Here I pick a frame of the countries with pop larger than one billion in 2007:

gap_sub <- gapminder %>% 
  filter(year == 2007 & pop > 100000000) %>% 
  droplevels()
knitr::kable(gap_sub)
country continent year lifeExp pop gdpPercap
Bangladesh Asia 2007 64.062 150448339 1391.254
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
India Asia 2007 64.698 1110396331 2452.210
Indonesia Asia 2007 70.650 223547000 3540.652
Japan Asia 2007 82.603 127467972 31656.068
Mexico Americas 2007 76.195 108700891 11977.575
Nigeria Africa 2007 46.859 135031164 2013.977
Pakistan Asia 2007 65.483 169270617 2605.948
United States Americas 2007 78.242 301139947 42951.653
Order according to mean of l ifeExp: 
gap_sub %>% 
  mutate(country = fct_reorder(country, lifeExp, mean)) %>% 
  knitr::kable()
country continent year lifeExp pop gdpPercap
Bangladesh Asia 2007 64.062 150448339 1391.254
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
India Asia 2007 64.698 1110396331 2452.210
Indonesia Asia 2007 70.650 223547000 3540.652
Japan Asia 2007 82.603 127467972 31656.068
Mexico Americas 2007 76.195 108700891 11977.575
Nigeria Africa 2007 46.859 135031164 2013.977
Pakistan Asia 2007 65.483 169270617 2605.948
United States Americas 2007 78.242 301139947 42951.653

And plot them above:

ggplot(gap_sub, aes(x = lifeExp, y = country, colour=country)) + 
  geom_point()

ggplot(gap_sub, aes(x = lifeExp, y = fct_reorder(country, lifeExp, mean),colour=country)) + 
  geom_point()

# ggplot(gap_sub, aes(x = lifeExp, y = order_back,colour=continent)) + 
#   geom_point()

We can notice that the order of the y-axis has changed. While the table was still shown in original order.

So what if we use arrange()?

gap_sub_arrange <- gap_sub %>%
  arrange(lifeExp)
knitr::kable(gap_sub_arrange)
country continent year lifeExp pop gdpPercap
Nigeria Africa 2007 46.859 135031164 2013.977
Bangladesh Asia 2007 64.062 150448339 1391.254
India Asia 2007 64.698 1110396331 2452.210
Pakistan Asia 2007 65.483 169270617 2605.948
Indonesia Asia 2007 70.650 223547000 3540.652
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
Mexico Americas 2007 76.195 108700891 11977.575
United States Americas 2007 78.242 301139947 42951.653
Japan Asia 2007 82.603 127467972 31656.068 
ggplot(gap_sub_arrange,aes(x = lifeExp, y = country, colour=country)) +
  geom_point()

If we arrange by lifeExp, we get the same order of countries in plot. But the order in figure has changed.

What if arrange() and fct_reorder() are combined?

gap_sub %>%
  arrange(lifeExp) %>% 
  mutate(country = fct_reorder(country, lifeExp, mean)) %>% 
  knitr::kable()
country continent year lifeExp pop gdpPercap
Nigeria Africa 2007 46.859 135031164 2013.977
Bangladesh Asia 2007 64.062 150448339 1391.254
India Asia 2007 64.698 1110396331 2452.210
Pakistan Asia 2007 65.483 169270617 2605.948
Indonesia Asia 2007 70.650 223547000 3540.652
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
Mexico Americas 2007 76.195 108700891 11977.575
United States Americas 2007 78.242 301139947 42951.653
Japan Asia 2007 82.603 127467972 31656.068 
gap_sub %>% 
  arrange(lifeExp) %>% 
  ggplot(aes(x = lifeExp, y = fct_reorder(country, lifeExp, mean), colour=country)) +
  geom_point()

Both are reordered.

Part 2: File I/O

Let’s reuse the gap_sub we’ve created in last part. ### write_csv()/read_csv()

gap_write <-gap_sub %>%
  arrange(lifeExp) %>% 
  mutate(country = fct_reorder(country, lifeExp, mean)) 
str(gap_write)
## Classes 'tbl_df', 'tbl' and 'data.frame':    10 obs. of  6 variables:
##  $ country  : Factor w/ 10 levels "Nigeria","Bangladesh",..: 1 2 3 4 5 6 7 8 9 10
##  $ continent: Factor w/ 3 levels "Africa","Americas",..: 1 3 3 3 3 2 3 2 2 3
##  $ year     : int  2007 2007 2007 2007 2007 2007 2007 2007 2007 2007
##  $ lifeExp  : num  46.9 64.1 64.7 65.5 70.7 ...
##  $ pop      : int  135031164 150448339 1110396331 169270617 223547000 190010647 1318683096 108700891 301139947 127467972
##  $ gdpPercap: num  2014 1391 2452 2606 3541 ...
knitr::kable(gap_write)
country continent year lifeExp pop gdpPercap
Nigeria Africa 2007 46.859 135031164 2013.977
Bangladesh Asia 2007 64.062 150448339 1391.254
India Asia 2007 64.698 1110396331 2452.210
Pakistan Asia 2007 65.483 169270617 2605.948
Indonesia Asia 2007 70.650 223547000 3540.652
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
Mexico Americas 2007 76.195 108700891 11977.575
United States Americas 2007 78.242 301139947 42951.653
Japan Asia 2007 82.603 127467972 31656.068 
write_csv(gap_write, "gap_write.csv")
gap_read_csv <- read_csv("gap_write.csv")
## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_integer(),
##   gdpPercap = col_double()
## )
knitr::kable(gap_read_csv)
country continent year lifeExp pop gdpPercap
Nigeria Africa 2007 46.859 135031164 2013.977
Bangladesh Asia 2007 64.062 150448339 1391.254
India Asia 2007 64.698 1110396331 2452.210
Pakistan Asia 2007 65.483 169270617 2605.948
Indonesia Asia 2007 70.650 223547000 3540.652
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
Mexico Americas 2007 76.195 108700891 11977.575
United States Americas 2007 78.242 301139947 42951.653
Japan Asia 2007 82.603 127467972 31656.068
When reading a d ataframe, wr ite_csv () parses column with t ypes. After read_csv(), the data keeps the same as before.

saveRDS()/readRDS()

saveRDS(gap_write, "gap_save.rds")
gap_read_rds <- readRDS("gap_save.rds")
knitr::kable(gap_read_rds)
country continent year lifeExp pop gdpPercap
Nigeria Africa 2007 46.859 135031164 2013.977
Bangladesh Asia 2007 64.062 150448339 1391.254
India Asia 2007 64.698 1110396331 2452.210
Pakistan Asia 2007 65.483 169270617 2605.948
Indonesia Asia 2007 70.650 223547000 3540.652
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
Mexico Americas 2007 76.195 108700891 11977.575
United States Americas 2007 78.242 301139947 42951.653
Japan Asia 2007 82.603 127467972 31656.068
The data surviv es the round trip.
### dput()/dget( ) 
dput(gap_write, "gap_put.txt")
gap_get_txt <- dget("gap_put.txt")
knitr::kable(gap_get_txt)
country continent year lifeExp pop gdpPercap
Nigeria Africa 2007 46.859 135031164 2013.977
Bangladesh Asia 2007 64.062 150448339 1391.254
India Asia 2007 64.698 1110396331 2452.210
Pakistan Asia 2007 65.483 169270617 2605.948
Indonesia Asia 2007 70.650 223547000 3540.652
Brazil Americas 2007 72.390 190010647 9065.801
China Asia 2007 72.961 1318683096 4959.115
Mexico Americas 2007 76.195 108700891 11977.575
United States Americas 2007 78.242 301139947 42951.653
Japan Asia 2007 82.603 127467972 31656.068
Also a great tri p with data survive d.

Part 3: Visualization design

Let’s make a plot with ggplot() first:

(plot <- gapminder %>% 
     filter(year==2007) %>% 
     ggplot(aes(pop, lifeExp)) +
     geom_point(aes(colour=gdpPercap), alpha=0.8) +
     scale_x_log10() +
     scale_colour_distiller(
         trans   = "log10",
         breaks  = 5^(1:20),
         palette = "Blues"
     ) +
     facet_wrap(~ continent) +
     scale_y_continuous(breaks=10*(1:10)) +
     theme_bw()) +
    theme(axis.text  = element_text(size=6),
          strip.background = element_rect(fill = "lightblue"),
    )

Now convert it to a plotly object:

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(plot)

Now we can select and zoom the plot, with exact data when hovering. We can also autoscale it to make the plot more focused. It is also possible to move each graph up and down, right and left. Or view it in 3-d format:

gapminder %>% 
filter(year==2007) %>% 
  plot_ly( 
        x = ~pop, 
        y = ~lifeExp, 
        z = ~gdpPercap,
        type = "scatter3d",
        mode = "markers",
        opacity = 0.2)

Part 4: Writing figures to file

Save the plot we made in part3:

ggsave("plot.png",plot)
## Saving 7 x 5 in image
Saved Plot

Saved Plot